
"""simple docstring"""


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			  =  {
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}



class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       Optional[Any]				=      '''gptj'''
       UpperCamelCase_		:       Dict				=      {
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }






       def __init__(	self			:				Optional[int]							,   UpperCAmelCase_			:				Optional[int]=50_400							,   UpperCAmelCase_			:				List[Any]=2_048							,   UpperCAmelCase_			:				Dict=4_096							,   UpperCAmelCase_			:				List[str]=28							,   UpperCAmelCase_			:				int=16							,   UpperCAmelCase_			:				Dict=64							,   UpperCAmelCase_			:				str=None							,   UpperCAmelCase_			:				List[Any]="gelu_new"							,   UpperCAmelCase_			:				Tuple=0.0							,   UpperCAmelCase_			:				List[str]=0.0							,   UpperCAmelCase_			:				int=0.0							,   UpperCAmelCase_			:				int=1e-5							,   UpperCAmelCase_			:				List[Any]=0.02							,   UpperCAmelCase_			:				Optional[int]=True							,   UpperCAmelCase_			:				Tuple=50_256							,   UpperCAmelCase_			:				Optional[int]=50_256							,   UpperCAmelCase_			:				str=False							,   **UpperCAmelCase_			:				int							,   )->	str:







              """simple docstring"""
              UpperCamelCase       			=		vocab_size
              UpperCamelCase       			=		n_positions
              UpperCamelCase       			=		n_embd
              UpperCamelCase       			=		n_layer
              UpperCamelCase       			=		n_head
              UpperCamelCase       			=		n_inner
              UpperCamelCase       			=		rotary_dim
              UpperCamelCase       			=		activation_function
              UpperCamelCase       			=		resid_pdrop
              UpperCamelCase       			=		embd_pdrop
              UpperCamelCase       			=		attn_pdrop
              UpperCamelCase       			=		layer_norm_epsilon
              UpperCamelCase       			=		initializer_range
              UpperCamelCase       			=		use_cache

              UpperCamelCase       			=		bos_token_id
              UpperCamelCase       			=		eos_token_id

              super().__init__(
                  bos_token_id=UpperCAmelCase_							,   eos_token_id=UpperCAmelCase_							,   tie_word_embeddings=UpperCAmelCase_							,   **UpperCAmelCase_      )





class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				str							,   UpperCAmelCase_			:				PretrainedConfig							,   UpperCAmelCase_			:				str = "default"							,   UpperCAmelCase_			:				List[PatchingSpec] = None							,   UpperCAmelCase_			:				bool = False							,   )->	Optional[Any]:







              """simple docstring"""
              super().__init__(UpperCAmelCase_							,   task=UpperCAmelCase_							,   patching_specs=UpperCAmelCase_							,   use_past=UpperCAmelCase_      )
              if not getattr(self._config							,   "pad_token_id"							,   UpperCAmelCase_      ):
                     # TODO: how to do that better?
                     UpperCamelCase       			=		0






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Mapping[str, Mapping[int, str]]:







              """simple docstring"""
              UpperCamelCase       			=		OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}      )
              if self.use_past:
                     self.fill_with_past_key_values_(UpperCAmelCase_							,   direction="inputs"      )
                     UpperCamelCase       			=		{0: "batch", 1: "past_sequence + sequence"}
              else:
                     UpperCamelCase       			=		{0: "batch", 1: "sequence"}

              return common_inputs






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	int:







              """simple docstring"""
              return self._config.n_layer






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	int:







              """simple docstring"""
              return self._config.n_head






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				PreTrainedTokenizer							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional[TensorType] = None							,   )->	Mapping[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		super(UpperCAmelCase_							,   self      ).generate_dummy_inputs(
                  UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   seq_length=UpperCAmelCase_							,   is_pair=UpperCAmelCase_							,   framework=UpperCAmelCase_      )

              # We need to order the input in the way they appears in the forward()
              UpperCamelCase       			=		OrderedDict({"input_ids": common_inputs["input_ids"]}      )

              # Need to add the past_keys
              if self.use_past:
                     if not is_torch_available():
                            raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."      )
                     else:
                            import torch

                            UpperCamelCase			,							UpperCamelCase       			=		common_inputs["input_ids"].shape
                            # Not using the same length for past_key_values
                            UpperCamelCase       			=		seqlen + 2
                            UpperCamelCase       			=		(
                                batch,
                                self.num_attention_heads,
                                past_key_values_length,
                                self._config.hidden_size // self.num_attention_heads,
                            )
                            UpperCamelCase       			=		[
                                (torch.zeros(UpperCAmelCase_      ), torch.zeros(UpperCAmelCase_      )) for _ in range(self.num_layers      )
                            ]

              UpperCamelCase       			=		common_inputs["attention_mask"]
              if self.use_past:
                     UpperCamelCase       			=		ordered_inputs["attention_mask"].dtype
                     UpperCamelCase       			=		torch.cat(
                         [ordered_inputs["attention_mask"], torch.ones(UpperCAmelCase_							,   UpperCAmelCase_							,   dtype=UpperCAmelCase_      )]							,   dim=1      )

              return ordered_inputs






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	int:







              """simple docstring"""
              return 13







"""simple docstring"""


import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE			  =  """cuda""" if torch.cuda.is_available() else """cpu"""



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=1_00		,   UpperCAmelCase_=" "					)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		text.split(UpperCAmelCase_					)
       return [character.join(text[i : i + n]					).strip() for i in range(0		,   len(UpperCAmelCase_					)		,   UpperCAmelCase_					)]



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase			,							UpperCamelCase       			=		[], []
       for title, text in zip(documents["title"]		,   documents["text"]					):
              if text is not None:
                     for passage in split_text(UpperCAmelCase_					):
                            titles.append(title if title is not None else ""					)
                            texts.append(UpperCAmelCase_					)
       return {"title": titles, "text": texts}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase       			=		ctx_tokenizer(
           documents["title"]		,   documents["text"]		,   truncation=UpperCAmelCase_		,   padding="longest"		,   return_tensors="pt"					)["input_ids"]
       UpperCamelCase       			=		ctx_encoder(input_ids.to(device=UpperCAmelCase_					)		,   return_dict=UpperCAmelCase_					).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   )->				List[str]:







       """simple docstring"""
       ######################################
       logger.info("Step 1 - Create the dataset"					)
       ######################################

       # The dataset needed for RAG must have three columns:
       # - title (string): title of the document
       # - text (string): text of a passage of the document
       # - embeddings (array of dimension d): DPR representation of the passage
       # Let's say you have documents in tab-separated csv files with columns "title" and "text"
       assert os.path.isfile(rag_example_args.csv_path					), "Please provide a valid path to a csv file"

       # You can load a Dataset object this way
       UpperCamelCase       			=		load_dataset(
           "csv"		,   data_files=[rag_example_args.csv_path]		,   split="train"		,   delimiter="\t"		,   column_names=["title", "text"]					)

       # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

       # Then split the documents into passages of 100 words
       UpperCamelCase       			=		dataset.map(UpperCAmelCase_		,   batched=UpperCAmelCase_		,   num_proc=processing_args.num_proc					)

       # And compute the embeddings
       UpperCamelCase       			=		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=UpperCAmelCase_					)
       UpperCamelCase       			=		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
       UpperCamelCase       			=		Features(
           {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
       UpperCamelCase       			=		dataset.map(
           partial(UpperCAmelCase_		,   ctx_encoder=UpperCAmelCase_		,   ctx_tokenizer=UpperCAmelCase_					)		,   batched=UpperCAmelCase_		,   batch_size=processing_args.batch_size		,   features=UpperCAmelCase_		,   )

       # And finally save your dataset
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset"					)
       dataset.save_to_disk(UpperCAmelCase_					)
       # from datasets import load_from_disk
       # dataset = load_from_disk(passages_path)  # to reload the dataset

       ######################################
       logger.info("Step 2 - Index the dataset"					)
       ######################################

       # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
       UpperCamelCase       			=		faiss.IndexHNSWFlat(index_hnsw_args.d		,   index_hnsw_args.m		,   faiss.METRIC_INNER_PRODUCT					)
       dataset.add_faiss_index("embeddings"		,   custom_index=UpperCAmelCase_					)

       # And save the index
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset_hnsw_index.faiss"					)
       dataset.get_index("embeddings"					).save(UpperCAmelCase_					)
       # dataset.load_faiss_index("embeddings", index_path)  # to reload the index



@dataclass
class 	__a	:
       UpperCamelCase_		:       str				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''						)     ,  metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=_lowerCAmelCase     ,  metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/rag-sequence-nq'''     ,  metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/dpr-ctx_encoder-multiset-base'''     ,  metadata={
               '''help''': (
                   '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
                   ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
               )
           }     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb'''						)     ,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       Optional[int]				=      field(
           default=_lowerCAmelCase     ,  metadata={
               '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
           }     ,  )
       UpperCamelCase_		:       int				=      field(
           default=16     ,  metadata={
               '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
           }     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       int				=      field(
           default=768     ,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}     ,  )
       UpperCamelCase_		:       int				=      field(
           default=128     ,  metadata={
               '''help''': (
                   '''The number of bi-directional links created for every new element during the HNSW index construction.'''
               )
           }     ,  )


if __name__ == "__main__":
      logging.basicConfig(level=logging.WARNING)
      logger.setLevel(logging.INFO)

      SCREAMING_SNAKE_CASE			  =  HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE			  =  parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE			  =  rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)





